UNCERTAINTY MODELING AND INTERPRETABILITY IN CONVOLUTIONAL NEURAL NETWORKS FOR POLYP SEGMENTATION
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Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motivated research in applications of such models for medical image analysis. If CNN-based models are to be helpful in a medical context, they need to be precise, interpretable, and uncertainty in predictions must be well understood. In this paper, we develop and evaluate recent advances in uncertainty estimation and model interpretability in the context of semantic segmentation of polyps from colonoscopy images. We evaluate and enhance several architectures of Fully Convolutional Networks (FCNs) for semantic segmentation of colorectal polyps and provide a comparison between these models. Our highest performing model achieves a 76.06% mean IOU accuracy on the EndoScene dataset, a considerable improvement over the previous state-of-the-art.
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Techniques for uncertainty estimation can also lessen deep neural networks' susceptibility to adversarial attacks.
“In addition to producing valuable measures that function as uncertainty measures [358, 359, 66], these techniques can also lessen deep neural networks susceptibility to adversarial attacks [357, 360].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [66]
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 2,720 · see citing works
“[50] 2018 FCN [51] and SehNet [44] polyps segmentation ×”
BackgroundAn overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works
Wickstrøm, K., Kampffmeyer, M., & Jenssen, R. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101619 · Cited by 185 · see citing works
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- Authors
- Wickstrom, K., Kampffmeyer, M., & Jenssen, R.
- Affiliations
- UiT The Arctic University of Norway (NO)
- Venue
- 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)
- Publisher
- IEEE
- Published
- 2018-09
- Pages
- 1-6
- ISSN
- 1551-2541 (ISSN-L)
- OpenAlex
- W2890068671
- Cited by
- 41
- References
- 4
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 6 · 2022: 3 · 2021: 6 · 2020: 7 · 2019: 9 · 2018: 2
- FWCI
- 12.92
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-sa
- Locations
- Machine learning for signal processing ... (publishedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- uncertainty estimation, model interpretability, convolutional neural networks, semantic segmentation, colorectal polyps, colonoscopy images, fully convolutional network, medical image analysis
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